Noninvasive biomarker for diagnosing major depressive disorder using optical coherence tomography

A method for predicting and diagnosing the occurrence of neurological diseases such as Multiple Sclerosis (MS), Alzheimer's disease, Parkinson's disease, and others in a human subject, the method comprising:

• (a) obtaining a plurality of features of various Retinal Layer thickness measurements using Optical Coherence Tomography (OCT) devices;
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• (b) selecting a combination of features from the plurality of features of various Retinal Layer thickness measurements based on biological analysis, traditional statistical analysis, and machine learning dimension reduction analysis;
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• (c) selecting a combination of features from the plurality of features of various Retinal Layer thickness measurements based on a robustness metric associated with insensitivity to manufacturing variabilities of Optical Coherence Tomography (OCT) devices and a performance metric associated with predicting the classification of neurological diseases such as Multiple Sclerosis (MS), Alzheimer's disease, Parkinson's disease, and others;
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• (d) training one or more machine learning models to predict and diagnose the classification of neurological diseases such as Multiple Sclerosis (MS), Alzheimer's disease, Parkinson's disease, and others using the combination of features measured by the Optical Coherence Tomography (OCT) devices.

A method for predicting and diagnosing the occurrence of major depressive disorder (MDD) in a human subject, the method comprising:

• (a) obtaining a plurality of features of various measurements of the gyri, including Cortical Mean Thickness, Inner Cortical Surface Area, Mid Cortical Surface Area, Pial Cortical Surface Area, Grey Matter (GM) Volume, Cerebrospinal Fluid (CSF) Volume, White Matter (WM) Volume, and Total Volume of the Occipital Lobe using voxel analysis of functional Magnetic Resonance Imaging (fMRI) scans;
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• (b) selecting a combination of features from the plurality of features of various measurements of the gyri of the Occipital Lobe based on biological analysis, traditional statistical analysis, and machine learning dimension reduction analysis;
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• (c) selecting a combination of features from the plurality of features of various measurements of the gyri of the Occipital Lobe based on a robustness metric associated with insensitivity to manufacturing variabilities of functional Magnetic Resonance Imaging (fMRI) devices and a performance metric associated with predicting a Major Depressive Disorder (MDD) classification;
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• (d) training one or more machine learning models to predict and diagnose the Major Depressive Disorder (MDD) classification using the combination of features measured by the functional Magnetic Resonance Imaging (fMRI) devices.